{
  "cells": [
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SJ9XKFGSblUR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "DtgV4TTQ71np"
      },
      "outputs": [],
      "source": [
        "# base model from huggingFace or path to model\n",
        "base_model = \"mistralai/Mistral-7B-Instruct-v0.2\"\n",
        "new_model = \"Enlighten_Instruct\"\n",
        "test_path='/content/Enlighten-Instruct/Dataset/TestData.csv'\n",
        "train_path='/content/Enlighten-Instruct/Dataset/TrainData.csv'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "fNZZilUV_L4r"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!git clone 'https://github.com/ali7919/Enlighten-Instruct.git'\n",
        "!pip install -U bitsandbytes\n",
        "!pip install transformers==4.36.2\n",
        "!pip install -U peft\n",
        "!pip install -U accelerate\n",
        "!pip install -U trl\n",
        "!pip install datasets==2.16.0\n",
        "!pip install sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "VLzgZ14X_rMs"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig,HfArgumentParser,TrainingArguments,pipeline, logging\n",
        "from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model\n",
        "import os,torch\n",
        "from datasets import load_dataset\n",
        "from trl import SFTTrainer\n",
        "import pandas as pd\n",
        "import pyarrow as pa\n",
        "import pyarrow.dataset as ds\n",
        "import pandas as pd\n",
        "from datasets import Dataset\n",
        "import re\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EB3dOioD71nr"
      },
      "source": [
        "# Login to HF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MwW_ql6WAE7T",
        "outputId": "1438d74b-bfcf-44cd-eb54-ef740e70c26e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "from google.colab import userdata\n",
        "secret_hf = userdata.get('HUGGINGFACE_TOKEN')\n",
        "!huggingface-cli login --token $secret_hf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cSm1bEkY71ns"
      },
      "source": [
        "# Build Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uPCfm2jxAE7U",
        "outputId": "56435ea8-de06-4fd2-be1c-c2c1e3c1508a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text'],\n",
              "    num_rows: 1910\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df = pd.read_csv(train_path)\n",
        "\n",
        "# build training dataset with the right format\n",
        "df['text'] = '<s>[INST]@Enlighten. ' + df['Q'] +'[/INST]'+ df['A'] + '</s>'\n",
        "\n",
        "# remove columns\n",
        "df=df.drop(['Q','A','class'],axis=1)\n",
        "\n",
        "# convert to dataset object\n",
        "dataset = ds.dataset(pa.Table.from_pandas(df).to_batches())\n",
        "dataset = Dataset(pa.Table.from_pandas(df))\n",
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9kEXGYa171ns"
      },
      "source": [
        "# Load the model"
      ]
    },
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      "execution_count": 6,
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        },
        "id": "StJKGiDDHzdk",
        "outputId": "c346feb7-0916-4a17-a8a7-369e640973d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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        {
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        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "91142b137c9d4e99b18b9bd43df5c20a"
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('<s>', '</s>')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Load base model\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit= True,\n",
        "    bnb_4bit_quant_type= \"nf4\",\n",
        "    bnb_4bit_compute_dtype= torch.bfloat16,\n",
        "    bnb_4bit_use_double_quant= False,\n",
        ")\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "        base_model,\n",
        "        load_in_4bit=True,\n",
        "        quantization_config=bnb_config,\n",
        "        torch_dtype=torch.bfloat16,\n",
        "        device_map=\"auto\",\n",
        "        trust_remote_code=True,\n",
        ")\n",
        "\n",
        "\n",
        "model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n",
        "model.config.pretraining_tp = 1\n",
        "model.gradient_checkpointing_enable()\n",
        "\n",
        "# Load tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n",
        "tokenizer.padding_side = 'right'\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.add_eos_token = True\n",
        "tokenizer.bos_token, tokenizer.eos_token\n"
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "jSRv5wDMAE7U",
        "outputId": "15d0ce4c-7d14-4724-fc8e-5b8e1967af8f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/776 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "221e01b9c0af44c18ee134573cf79eab"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ],
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              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc42c6c46fc64692970dfea16ed898ca"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "962734"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# count trainging tokens\n",
        "from transformers import LlamaTokenizer\n",
        "tokenizer_ = LlamaTokenizer.from_pretrained(\"cognitivecomputations/dolphin-llama2-7b\")\n",
        "tokens = tokenizer_.tokenize(dataset.to_pandas().to_string())\n",
        "len(tokens)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kwEw2EpM71nt"
      },
      "source": [
        "# Fine-Tune"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "DZoClwHcPTqQ"
      },
      "outputs": [],
      "source": [
        "#Adding the adapters in the layers\n",
        "model = prepare_model_for_kbit_training(model)\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=16,\n",
        "    lora_dropout=0.1,\n",
        "    r=64,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\"gate_proj\"]\n",
        ")\n",
        "model = get_peft_model(model, peft_config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "peOnLAAhS0y1"
      },
      "outputs": [],
      "source": [
        "#Hyperparamter\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    num_train_epochs=1,\n",
        "    per_device_train_batch_size=4,\n",
        "    gradient_accumulation_steps=1,\n",
        "    optim=\"paged_adamw_32bit\",\n",
        "    save_steps=50,\n",
        "    logging_steps=1,\n",
        "    learning_rate=2e-4,\n",
        "    weight_decay=0.001,\n",
        "    fp16=False,\n",
        "    bf16=False,\n",
        "    max_grad_norm=0.3,\n",
        "    max_steps=-1,\n",
        "    warmup_ratio=0.03,\n",
        "    group_by_length=True,\n",
        "    lr_scheduler_type=\"constant\",\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "1340061e656b4fa9ba6e24045b908ac8",
            "f805cf8490824a5688c9ef6dea6e7581",
            "56938860489a40549ff26688b23e22f1",
            "b1f6533a8e12495c8a861dc8ed20fa4a",
            "fa3c470a66ba4a61b10a37567d7603e9",
            "0ad8656f71bd4ca891e008f0b53f4e5b",
            "e8be3090de4643158c597fce18d043e0",
            "37cc8c6a4e3e405ca8c9dd606760d70a",
            "7b5221af24c44a6aab1c1092a3ec5e03",
            "88392d805f6a42cbb922e43f0b65dca7",
            "e9e36e05a76449058c6b9dc2c5922689"
          ]
        },
        "id": "nLWq2yIZAE7U",
        "outputId": "55fa2bf4-8c43-414f-ebf7-34cb4d9f9c30"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:225: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1910 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1340061e656b4fa9ba6e24045b908ac8"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Setting sft parameters\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset,\n",
        "    peft_config=peft_config,\n",
        "    max_seq_length= None,\n",
        "    dataset_text_field=\"text\",\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing= False,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "r5EgWxuuAE7U",
        "outputId": "8086b263-c7d1-4f09-b538-1609893c67cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='258' max='478' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [258/478 31:00 < 26:39, 0.14 it/s, Epoch 0.54/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.542400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.718500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.074700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.765000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.581900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.418300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.152400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.000400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.983500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.814400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>1.969600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>1.662500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>1.676600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>1.487200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>1.880000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>2.178900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>1.801100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>1.805900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>1.671900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.897500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>1.781500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>1.642100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>1.632800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>1.693500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>1.877200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>1.501700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>1.527400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>1.842300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>1.442100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.646600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>1.694600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>1.603500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>1.336100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>1.836600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>1.593500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>1.570000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>1.440000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>1.511400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>1.318400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.496100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>1.631900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>1.461300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>1.210100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>1.723000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>1.815400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>1.449900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>1.569700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>1.428800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>1.672100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.777500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>0.962700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>1.253900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>1.427200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>1.271500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>1.431600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>1.090400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>1.124100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>1.304200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>1.463700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.132700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>1.020400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>0.949500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>1.332000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>0.970900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>1.273400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>1.164000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>1.222000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>1.170200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>0.992000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>1.208700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>1.324000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>1.141600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73</td>\n",
              "      <td>1.429400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74</td>\n",
              "      <td>1.151900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>1.504500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76</td>\n",
              "      <td>1.108100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77</td>\n",
              "      <td>1.134900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78</td>\n",
              "      <td>0.951000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79</td>\n",
              "      <td>1.138700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.014600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81</td>\n",
              "      <td>1.156200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>1.247600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83</td>\n",
              "      <td>1.028800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84</td>\n",
              "      <td>0.932200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85</td>\n",
              "      <td>1.247800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86</td>\n",
              "      <td>1.205000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87</td>\n",
              "      <td>1.023000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88</td>\n",
              "      <td>1.307900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89</td>\n",
              "      <td>0.863600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>1.009400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91</td>\n",
              "      <td>0.977400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92</td>\n",
              "      <td>1.358100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>1.115900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94</td>\n",
              "      <td>1.083300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95</td>\n",
              "      <td>1.059700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96</td>\n",
              "      <td>0.911000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97</td>\n",
              "      <td>0.992400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98</td>\n",
              "      <td>1.011800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99</td>\n",
              "      <td>0.962900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.276100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>101</td>\n",
              "      <td>0.904000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>102</td>\n",
              "      <td>1.019500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>103</td>\n",
              "      <td>0.966900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>104</td>\n",
              "      <td>1.288400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>105</td>\n",
              "      <td>0.966200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>106</td>\n",
              "      <td>1.068000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>107</td>\n",
              "      <td>0.985300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>108</td>\n",
              "      <td>1.148900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>109</td>\n",
              "      <td>1.021800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>1.080600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>111</td>\n",
              "      <td>1.070000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>112</td>\n",
              "      <td>1.033700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>113</td>\n",
              "      <td>1.262900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>114</td>\n",
              "      <td>1.121800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>115</td>\n",
              "      <td>0.990500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>116</td>\n",
              "      <td>1.056200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>117</td>\n",
              "      <td>0.978100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>118</td>\n",
              "      <td>1.171900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>119</td>\n",
              "      <td>0.799700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>1.122000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>121</td>\n",
              "      <td>1.070300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>122</td>\n",
              "      <td>1.176100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>123</td>\n",
              "      <td>0.886700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>124</td>\n",
              "      <td>0.902300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>0.956200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>126</td>\n",
              "      <td>1.138600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>127</td>\n",
              "      <td>0.800500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>128</td>\n",
              "      <td>0.893700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>129</td>\n",
              "      <td>1.012300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>1.098900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>131</td>\n",
              "      <td>0.961900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>132</td>\n",
              "      <td>1.153200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>133</td>\n",
              "      <td>1.021900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>134</td>\n",
              "      <td>1.026700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>135</td>\n",
              "      <td>1.041600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>136</td>\n",
              "      <td>1.181600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>137</td>\n",
              "      <td>0.936600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>138</td>\n",
              "      <td>0.924100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>139</td>\n",
              "      <td>0.989700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>1.200300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>141</td>\n",
              "      <td>1.244400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>142</td>\n",
              "      <td>0.992200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>143</td>\n",
              "      <td>1.096000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>144</td>\n",
              "      <td>0.976700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>145</td>\n",
              "      <td>0.953600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>146</td>\n",
              "      <td>0.975700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>147</td>\n",
              "      <td>1.302800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>148</td>\n",
              "      <td>0.942200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>149</td>\n",
              "      <td>1.020500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.336800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>151</td>\n",
              "      <td>0.918400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>152</td>\n",
              "      <td>0.931500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>153</td>\n",
              "      <td>0.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>154</td>\n",
              "      <td>1.035500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>155</td>\n",
              "      <td>0.835700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>156</td>\n",
              "      <td>1.165300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>157</td>\n",
              "      <td>1.030400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>158</td>\n",
              "      <td>0.758100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>159</td>\n",
              "      <td>0.943800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>0.843600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>161</td>\n",
              "      <td>0.862900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>162</td>\n",
              "      <td>0.751900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>163</td>\n",
              "      <td>0.741200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>164</td>\n",
              "      <td>0.891100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>165</td>\n",
              "      <td>1.074100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>166</td>\n",
              "      <td>0.773000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>167</td>\n",
              "      <td>1.224100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>168</td>\n",
              "      <td>0.927500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>169</td>\n",
              "      <td>0.902000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170</td>\n",
              "      <td>0.842200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>171</td>\n",
              "      <td>0.851600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>172</td>\n",
              "      <td>0.839400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>173</td>\n",
              "      <td>1.199500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>174</td>\n",
              "      <td>0.748900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>0.835500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>176</td>\n",
              "      <td>0.865500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>177</td>\n",
              "      <td>1.171600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>178</td>\n",
              "      <td>0.929600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>179</td>\n",
              "      <td>0.768600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>0.926200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>181</td>\n",
              "      <td>0.945900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>182</td>\n",
              "      <td>0.802900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>183</td>\n",
              "      <td>1.173600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>184</td>\n",
              "      <td>0.818200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>185</td>\n",
              "      <td>0.870100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>186</td>\n",
              "      <td>1.120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>187</td>\n",
              "      <td>0.898300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>188</td>\n",
              "      <td>1.081900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>189</td>\n",
              "      <td>1.054300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190</td>\n",
              "      <td>1.071700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>191</td>\n",
              "      <td>0.978900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>192</td>\n",
              "      <td>0.769300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>193</td>\n",
              "      <td>0.822700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>194</td>\n",
              "      <td>0.931200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>195</td>\n",
              "      <td>0.794400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>196</td>\n",
              "      <td>0.909000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>197</td>\n",
              "      <td>0.610800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>198</td>\n",
              "      <td>1.210700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>199</td>\n",
              "      <td>0.857700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.055800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>201</td>\n",
              "      <td>0.777500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>202</td>\n",
              "      <td>0.694600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>203</td>\n",
              "      <td>0.564600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>204</td>\n",
              "      <td>0.741600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>205</td>\n",
              "      <td>0.907200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>206</td>\n",
              "      <td>1.196100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>207</td>\n",
              "      <td>1.130600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>208</td>\n",
              "      <td>0.633400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>209</td>\n",
              "      <td>0.777200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>210</td>\n",
              "      <td>0.694700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>211</td>\n",
              "      <td>0.896600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>212</td>\n",
              "      <td>0.824000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>213</td>\n",
              "      <td>0.973200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>214</td>\n",
              "      <td>1.003900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>215</td>\n",
              "      <td>1.049200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>216</td>\n",
              "      <td>0.817400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>217</td>\n",
              "      <td>0.786100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>218</td>\n",
              "      <td>1.016500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>219</td>\n",
              "      <td>0.995800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220</td>\n",
              "      <td>0.825300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>221</td>\n",
              "      <td>0.632600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>222</td>\n",
              "      <td>0.994800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>223</td>\n",
              "      <td>0.754000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>224</td>\n",
              "      <td>1.014700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>225</td>\n",
              "      <td>0.774800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>226</td>\n",
              "      <td>0.922700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>227</td>\n",
              "      <td>0.876000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>228</td>\n",
              "      <td>0.842700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>229</td>\n",
              "      <td>0.740200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>230</td>\n",
              "      <td>0.835100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>231</td>\n",
              "      <td>0.791700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>232</td>\n",
              "      <td>0.884400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>233</td>\n",
              "      <td>1.025300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>234</td>\n",
              "      <td>0.764600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>235</td>\n",
              "      <td>1.161600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>236</td>\n",
              "      <td>0.789700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>237</td>\n",
              "      <td>0.680400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>238</td>\n",
              "      <td>0.910100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>239</td>\n",
              "      <td>0.711500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>1.064900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>241</td>\n",
              "      <td>0.757800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>242</td>\n",
              "      <td>1.022100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>243</td>\n",
              "      <td>0.701900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>244</td>\n",
              "      <td>0.857700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>245</td>\n",
              "      <td>1.064600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>246</td>\n",
              "      <td>0.859400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>247</td>\n",
              "      <td>1.152700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>248</td>\n",
              "      <td>0.886100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>249</td>\n",
              "      <td>1.140700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>0.795600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>251</td>\n",
              "      <td>0.777900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>252</td>\n",
              "      <td>0.763700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>253</td>\n",
              "      <td>1.100300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>254</td>\n",
              "      <td>0.706400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>255</td>\n",
              "      <td>0.896900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>256</td>\n",
              "      <td>0.959100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-3435b262f1ae>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trl_activate_neftune\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 331\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# After training we make sure to retrieve back the original forward pass method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1535\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1536\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1537\u001b[0;31m             return inner_training_loop(\n\u001b[0m\u001b[1;32m   1538\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1539\u001b[0m                 \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1852\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1853\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccumulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1854\u001b[0;31m                     \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1855\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1856\u001b[0m                 if (\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   2742\u001b[0m                 \u001b[0mscaled_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2743\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2744\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2745\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2746\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgradient_accumulation_steps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, loss, **kwargs)\u001b[0m\n\u001b[1;32m   1964\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1965\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1966\u001b[0;31m             \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1967\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1968\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_trigger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    490\u001b[0m                 \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m             )\n\u001b[0;32m--> 492\u001b[0;31m         torch.autograd.backward(\n\u001b[0m\u001b[1;32m    493\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    249\u001b[0m     \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m     \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m     Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    252\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m         \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlBgQqor71nt"
      },
      "source": [
        "# Save and push the adapter to HF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nKgZBEGVS5a2",
        "outputId": "47dbeeb1-5acb-4fab-f271-6040a9c91b5d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PeftModelForCausalLM(\n",
              "  (base_model): LoraModel(\n",
              "    (model): MistralForCausalLM(\n",
              "      (model): MistralModel(\n",
              "        (embed_tokens): Embedding(32000, 4096)\n",
              "        (layers): ModuleList(\n",
              "          (0-31): 32 x MistralDecoderLayer(\n",
              "            (self_attn): MistralAttention(\n",
              "              (q_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=64, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=64, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (k_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=64, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=64, out_features=1024, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (v_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=64, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=64, out_features=1024, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (o_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=64, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=64, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (rotary_emb): MistralRotaryEmbedding()\n",
              "            )\n",
              "            (mlp): MistralMLP(\n",
              "              (gate_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=64, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=64, out_features=14336, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (up_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n",
              "              (down_proj): Linear4bit(in_features=14336, out_features=4096, bias=False)\n",
              "              (act_fn): SiLU()\n",
              "            )\n",
              "            (input_layernorm): MistralRMSNorm()\n",
              "            (post_attention_layernorm): MistralRMSNorm()\n",
              "          )\n",
              "        )\n",
              "        (norm): MistralRMSNorm()\n",
              "      )\n",
              "      (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# Save the fine-tuned model\n",
        "trainer.model.save_pretrained(new_model)\n",
        "model.config.use_cache = True\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85,
          "referenced_widgets": [
            "4bc9cc1b80e54dfbb3b4fc7b382a2ae5",
            "837e5c1c2618456eaed8beaa19abfd25",
            "97d06cb8365a403d9a399f3b395d5dda",
            "06c1f58eb8704d418ff16bb2e5f0d8af",
            "c094a2990d40417a8cd9cce3482b17dc",
            "a49c0656ddf24b8f98e5dc2881158cf2",
            "57eee5fe60c140f0a307d844a047dad8",
            "aff5f30aa9354817b76ac9599db8a507",
            "391b4eb55f054fb6b99673024843605b",
            "d3447c75163745a6a270fb3e0a55c165",
            "49bde0aaf4794cfb835f7a188d1689e9"
          ]
        },
        "id": "WDGk9dI0AE7U",
        "outputId": "9171714b-62a7-4f23-ad9c-9dffb34cf7a2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/369M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4bc9cc1b80e54dfbb3b4fc7b382a2ae5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/darshil3011/Enlighten_Instruct/commit/9984e81e01eab81439a27bfa076ab38a24e29cba', commit_message='Upload model', commit_description='', oid='9984e81e01eab81439a27bfa076ab38a24e29cba', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "trainer.model.push_to_hub(new_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_ncORsl71nt"
      },
      "source": [
        "# Test the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ThPFD1zZAE7U"
      },
      "outputs": [],
      "source": [
        "logging.set_verbosity(logging.CRITICAL)\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "d6Axoojg71nt"
      },
      "outputs": [],
      "source": [
        "def build_prompt(question):\n",
        "  prompt=f\"<s>[INST]@Enlighten. {question} [/INST]\"\n",
        "  return prompt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "wsWt_lJbAE7U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8295325a-56c0-4ec5-97bf-d07634a361ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s>[INST]@Enlighten. what is a computer ? [/INST]A computer is a device that can be programmed to carry out a complex set of instructions. In this game, a computer is a device that can be used to store and retrieve data. It can be accessed by the player to view information about the game world. It can also be used to store and retrieve data for the player. The computer is a simple device that can be accessed by the player to view information about the game world. It can also be used to store and retrieve data for the player. It is a simple device that can be accessed by the player to view information about the game world. It can also be used to store and retrieve data for the player. It is a simple device that can be accessed by the player to view information about the game world. It can also be used to store and retrieve data for the player. It is a simple device that can be accessed by the player\n"
          ]
        }
      ],
      "source": [
        "question = \"what is a computer ?\"\n",
        "prompt = build_prompt(question)\n",
        "result = pipe(prompt)\n",
        "\n",
        "print(result[0]['generated_text'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "QNhtTMafAE7V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7f4bc1c2-d4d4-4f8d-b550-c428d5a129d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#############################\n",
            "Answer the following question, at the end of your response write the answer like this: Answer:a or Answer:b or Answer:c or Answer:d \n",
            "What does the BeastAttack class do in the Unity project?\n",
            "a)Manages player movement\n",
            "b)Generates sounds\n",
            "c)Manages the beast attack behavior and player hit behavior\n",
            "d)Controls the camera\n",
            "first think step by step\n",
            "\n",
            "LLM Answer:\n",
            "c)Manages the beast attack behavior and player hit behavior\n",
            "\n",
            "Explanation:\n",
            "The BeastAttack class is responsible for managing the beast attack behavior and player hit behavior. It is a MonoBehaviour component that is attached to the beast game object. It has a reference to the player game object and manages the attack collider and the player hit collider. It also has a reference to the player health manager and manages the player health. It has a reference\n",
            "LLM Answer:\n",
            "c)Manages the beast attack behavior and player hit behavior \n",
            "Answer:c\n",
            "correct\n",
            "Progress: 0.0037174721189591076\n",
            "Accuracy: 1.0\n",
            "#############################\n",
            "Answer the following question, at the end of your response write the answer like this: Answer:a or Answer:b or Answer:c or Answer:d \n",
            "What does the gameObject that uses the BeastAttack.cs script need to have on it?\n",
            "a)A Rigidbody\n",
            "b)A Collider\n",
            "c)A Cinemachine Impulse Source\n",
            "d)All of the above\n",
            "first think step by step\n",
            "\n",
            "LLM Answer:\n",
            "The gameObject that uses the BeastAttack.cs script needs to have a Rigidbody and a Collider on it.\n",
            "Answer:b and a.\n",
            "Explanation:\n",
            "The beast needs to have a Rigidbody and a Collider on it to be able to move and interact with the environment.\n",
            "The Cinemachine Impulse Source is not required for the beast to attack, but it can be used to add a\n",
            "wrong\n",
            "Progress: 0.007434944237918215\n",
            "Accuracy: 0.5\n",
            "#############################\n",
            "Answer the following question, at the end of your response write the answer like this: Answer:a or Answer:b or Answer:c or Answer:d \n",
            "What happens when the player gets hit while in the beast's Escaping state?\n",
            "a)The player is pushed back\n",
            "b)The player is pushed back and the beast enters a cooldown state\n",
            "c)The player is pushed back and the beast dies\n",
            "d)The player is not affected\n",
            "first think step by step\n",
            "\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-f7c748a131e9>\u001b[0m in \u001b[0;36m<cell line: 16>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0;31m#generate answer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m         \u001b[0mllmAnswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'generated_text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/text_generation.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, text_inputs, **kwargs)\u001b[0m\n\u001b[1;32m    206\u001b[0m               \u001b[0mids\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgenerated\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \"\"\"\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m     def preprocess(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, num_workers, batch_size, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1138\u001b[0m             )\n\u001b[1;32m   1139\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1140\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_single\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreprocess_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforward_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpostprocess_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1142\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrun_multi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreprocess_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforward_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpostprocess_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py\u001b[0m in \u001b[0;36mrun_single\u001b[0;34m(self, inputs, preprocess_params, forward_params, postprocess_params)\u001b[0m\n\u001b[1;32m   1145\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrun_single\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreprocess_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforward_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpostprocess_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1146\u001b[0m         \u001b[0mmodel_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpreprocess_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1147\u001b[0;31m         \u001b[0mmodel_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mforward_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1148\u001b[0m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpostprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpostprocess_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, model_inputs, **forward_params)\u001b[0m\n\u001b[1;32m   1044\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0minference_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1045\u001b[0m                     \u001b[0mmodel_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_tensor_on_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1046\u001b[0;31m                     \u001b[0mmodel_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mforward_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1047\u001b[0m                     \u001b[0mmodel_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_tensor_on_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1048\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/text_generation.py\u001b[0m in \u001b[0;36m_forward\u001b[0;34m(self, model_inputs, **generate_kwargs)\u001b[0m\n\u001b[1;32m    269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0;31m# BS x SL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 271\u001b[0;31m         \u001b[0mgenerated_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mgenerate_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    272\u001b[0m         \u001b[0mout_b\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerated_sequence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1138\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneration_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneration_config\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1139\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1140\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1141\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1142\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_inputs_for_generation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_model_prepare_inputs_for_generation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, **kwargs)\u001b[0m\n\u001b[1;32m   1716\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mgeneration_mode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mGenerationMode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGREEDY_SEARCH\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1717\u001b[0m             \u001b[0;31m# 11. run greedy search\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1718\u001b[0;31m             return self.greedy_search(\n\u001b[0m\u001b[1;32m   1719\u001b[0m                 \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1720\u001b[0m                 \u001b[0mlogits_processor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogits_processor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py\u001b[0m in \u001b[0;36mgreedy_search\u001b[0;34m(self, input_ids, logits_processor, stopping_criteria, max_length, pad_token_id, eos_token_id, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, synced_gpus, streamer, **model_kwargs)\u001b[0m\n\u001b[1;32m   2577\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2578\u001b[0m             \u001b[0;31m# forward pass to get next token\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2579\u001b[0;31m             outputs = self(\n\u001b[0m\u001b[1;32m   2580\u001b[0m                 \u001b[0;34m**\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2581\u001b[0m                 \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/hooks.py\u001b[0m in \u001b[0;36mnew_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    164\u001b[0m                 \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_old_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_old_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    167\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hf_hook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/mistral/modeling_mistral.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1051\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1052\u001b[0m         \u001b[0;31m# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1053\u001b[0;31m         outputs = self.model(\n\u001b[0m\u001b[1;32m   1054\u001b[0m             \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1055\u001b[0m             \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/hooks.py\u001b[0m in \u001b[0;36mnew_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    164\u001b[0m                 \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_old_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_old_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    167\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hf_hook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/mistral/modeling_mistral.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    936\u001b[0m                 )\n\u001b[1;32m    937\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 938\u001b[0;31m                 layer_outputs = decoder_layer(\n\u001b[0m\u001b[1;32m    939\u001b[0m                     \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    940\u001b[0m                     \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/mistral/modeling_mistral.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, position_ids, past_key_value, output_attentions, use_cache, **kwargs)\u001b[0m\n\u001b[1;32m    661\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    662\u001b[0m         \u001b[0;31m# Self Attention\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 663\u001b[0;31m         hidden_states, self_attn_weights, present_key_value = self.self_attn(\n\u001b[0m\u001b[1;32m    664\u001b[0m             \u001b[0mhidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    665\u001b[0m             \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/mistral/modeling_mistral.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, position_ids, past_key_value, output_attentions, use_cache, **kwargs)\u001b[0m\n\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0mquery_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mq_proj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m         \u001b[0mkey_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mk_proj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    257\u001b[0m         \u001b[0mvalue_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mv_proj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/tuners/lora/bnb.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, *args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 311\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m                 \u001b[0;31m# As per Tim Dettmers, for 4bit, we need to defensively clone here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m                 \u001b[0;31m# The reason is that in some cases, an error can occur that backprop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/hooks.py\u001b[0m in \u001b[0;36mnew_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    164\u001b[0m                 \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_old_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_old_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    167\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hf_hook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0mbias\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbnb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul_4bit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquant_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquant_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minp_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py\u001b[0m in \u001b[0;36mmatmul_4bit\u001b[0;34m(A, B, quant_state, out, bias)\u001b[0m\n\u001b[1;32m    570\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mMatMul4Bit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquant_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgemv_4bit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mquant_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m                 \u001b[0mout\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/bitsandbytes/functional.py\u001b[0m in \u001b[0;36mgemv_4bit\u001b[0;34m(A, B, out, transposed_A, transposed_B, state)\u001b[0m\n\u001b[1;32m   1640\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Matmul not implemented for data type {A.dtype}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1642\u001b[0;31m     \u001b[0mpost_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprev_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1643\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1644\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/bitsandbytes/functional.py\u001b[0m in \u001b[0;36mpost_call\u001b[0;34m(prev_device)\u001b[0m\n\u001b[1;32m    420\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    421\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpost_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprev_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 422\u001b[0;31m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprev_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    424\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36mset_device\u001b[0;34m(device)\u001b[0m\n\u001b[1;32m    400\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mthis\u001b[0m \u001b[0margument\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mnegative\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    401\u001b[0m     \"\"\"\n\u001b[0;32m--> 402\u001b[0;31m     \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_device_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    403\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdevice\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_setDevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "df_test=pd.read_csv(test_path)\n",
        "\n",
        "questionCounter=0\n",
        "correct=0\n",
        "promptEnding = \"[/INST]\"\n",
        "\n",
        "# this must be >= 2\n",
        "fail_limit=10\n",
        "\n",
        "# chain of thought activator, model might run out of output tokens\n",
        "USE_COT=True\n",
        "\n",
        "#this comes before the question\n",
        "testGuide='Answer the following question, at the end of your response write the answer like this: Answer:a or Answer:b or Answer:c or Answer:d \\n'\n",
        "\n",
        "for index, row in df_test.iterrows():\n",
        "    print(\"#############################\")\n",
        "    questionCounter = questionCounter + 1\n",
        "\n",
        "    #chain of thought activator\n",
        "    if USE_COT:\n",
        "        chainOfThoughtActivator='\\nfirst think step by step\\n'\n",
        "    else:\n",
        "        chainOfThoughtActivator='\\n'\n",
        "\n",
        "    #build the prompt\n",
        "    question=testGuide + row['Question'] + '\\na)' + row['a'] + '\\nb)' + row['b'] + '\\nc)' + row['c'] + '\\nd)' + row['d'] + chainOfThoughtActivator\n",
        "    print(question)\n",
        "\n",
        "    #true answer\n",
        "    truth=row['Answer']\n",
        "\n",
        "    #use a loop, if llm stopped before reaching the answer. ask again\n",
        "    index=-1\n",
        "    failCounter=0\n",
        "    while(index==-1):\n",
        "\n",
        "        #build the prompt\n",
        "        prompt = build_prompt(question)\n",
        "\n",
        "        #generate answer\n",
        "        result = pipe(prompt)\n",
        "        llmAnswer = result[0]['generated_text']\n",
        "\n",
        "        #remove our prompt from it\n",
        "        index = llmAnswer.find(promptEnding)\n",
        "        llmAnswer = llmAnswer[len(promptEnding)+index:]\n",
        "\n",
        "        print(\"LLM Answer:\")\n",
        "        print(llmAnswer)\n",
        "\n",
        "        #remove spaces\n",
        "        llmAnswer=llmAnswer.replace(' ','')\n",
        "\n",
        "        #find the option in response\n",
        "        index = llmAnswer.find('Answer:')\n",
        "\n",
        "        #edge case - llm stoped at the worst time\n",
        "        if(index+len('Answer:')==len(llmAnswer)):\n",
        "            index=-1\n",
        "\n",
        "        #update question for the next try. remove chain of thought\n",
        "        question=testGuide + row['Question'] + '\\na)' + row['a'] + '\\nb)' + row['b'] + '\\nc)' + row['c'] + '\\nd)' + row['d']\n",
        "\n",
        "        #Don't get stock on a question\n",
        "        failCounter=failCounter+1\n",
        "        if failCounter==fail_limit:\n",
        "            break\n",
        "\n",
        "    if failCounter==fail_limit:\n",
        "        continue\n",
        "\n",
        "    #find and match the option\n",
        "    next_char = llmAnswer[index+len('Answer:'):][0]\n",
        "    if next_char in truth:\n",
        "        correct=correct+1\n",
        "        print('correct')\n",
        "    else:\n",
        "        print('wrong')\n",
        "\n",
        "    #update accuracy\n",
        "    accuracy=correct/questionCounter\n",
        "    print(f\"Progress: {questionCounter/len(df_test)}\")\n",
        "    print(f\"Accuracy: {accuracy}\")\n",
        "\n",
        "\n"
      ]
    },
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        "--------------------------"
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